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• W2587673654 abstract "In recent years, we can observe an increasing use of biometric technology in our daily lives. Face recognition has several advantages over other biometric modalities, since that it is natural, nonintrusive, and it is a task that humans perform routinely and effortlessly. Following a recent trend in this research field, this paper focuses on a part-based face recognition, exploring and evaluating specific descriptions / classifications for each facial part. Experimental results obtained in three public datasets (AR Face, MUCT and XM2VTS), assessing 15 approaches in each facial part and two score fusion strategies, show that features and classifiers specific for facial part can improve the accuracy of biometric systems, achieving error rates close to zero in some cases, including scenarios where the false acceptance cases are critical." @default.
• W2587673654 created "2017-02-17" @default.
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• W2587673654 creator A5081774195 @default.
• W2587673654 date "2016-10-01" @default.
• W2587673654 modified "2023-09-30" @default.
• W2587673654 title "Part-based representation and classification for face recognition" @default.
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• W2587673654 doi "https://doi.org/10.1109/smc.2016.7844680" @default.
• W2587673654 hasPublicationYear "2016" @default.
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